{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "if not any(path.endswith('textbook') for path in sys.path):\n",
    "    sys.path.append(os.path.abspath('../../..'))\n",
    "from textbook_utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joining\n",
    "\n",
    "Data scientists very frequently want to *join* two or more dataframes together\n",
    "in order to connect data values across dataframes. For instance, an online\n",
    "bookstore might have one dataframe with the books each user has ordered and a\n",
    "second dataframe with the genres of each book. By joining the two dataframes\n",
    "together, the data scientist can see what genres each user prefers.\n",
    "\n",
    "We'll continue looking at the baby names data. We'll use joins to check some\n",
    "trends mentioned in the [_New York Times_ article about baby names](https://www.nytimes.com/2021/06/12/style/lilibet-popular-baby-names.html). The article talks about how certain categories of\n",
    "names have become more or less popular over time. For instance, it mentions\n",
    "that mythological names like Julius and Cassius have become popular, while baby\n",
    "boomer names like Susan and Debbie have become less popular. How has the\n",
    "popularity of these categories changed over time?\n",
    "\n",
    "We've taken the names and categories in the _NYT_ article and put them in a small\n",
    "dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nyt_name</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lucifer</td>\n",
       "      <td>forbidden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lilith</td>\n",
       "      <td>forbidden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Danger</td>\n",
       "      <td>forbidden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Venus</td>\n",
       "      <td>celestial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Celestia</td>\n",
       "      <td>celestial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Skye</td>\n",
       "      <td>celestial</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    nyt_name   category\n",
       "0    Lucifer  forbidden\n",
       "1     Lilith  forbidden\n",
       "2     Danger  forbidden\n",
       "..       ...        ...\n",
       "20     Venus  celestial\n",
       "21  Celestia  celestial\n",
       "22      Skye  celestial\n",
       "\n",
       "[23 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyt = pd.read_csv('nyt_names.csv')\n",
    "nyt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see how popular the categories of names are, we join the `nyt` dataframe\n",
    "with the `baby` dataframe to get the name counts from `baby`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Liam</td>\n",
       "      <td>M</td>\n",
       "      <td>19659</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Noah</td>\n",
       "      <td>M</td>\n",
       "      <td>18252</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oliver</td>\n",
       "      <td>M</td>\n",
       "      <td>14147</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020719</th>\n",
       "      <td>Verona</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020720</th>\n",
       "      <td>Vertie</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020721</th>\n",
       "      <td>Wilma</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2020722 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name Sex  Count  Year\n",
       "0          Liam   M  19659  2020\n",
       "1          Noah   M  18252  2020\n",
       "2        Oliver   M  14147  2020\n",
       "...         ...  ..    ...   ...\n",
       "2020719  Verona   F      5  1880\n",
       "2020720  Vertie   F      5  1880\n",
       "2020721   Wilma   F      5  1880\n",
       "\n",
       "[2020722 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby = pd.read_csv('babynames.csv')\n",
    "baby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For intuition, we can imagine going down each row in `baby` and asking: is this name in the `nyt` table? If so, then add the value in the `category` column to the row. This is the basic idea behind a join. Let's look at a few examples on smaller dataframes first."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inner Joins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by making smaller versions of the `baby` and `nyt` tables so that it's easier to see what happens when we join tables together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "nyt_small = nyt.iloc[[11, 12, 14]].reset_index(drop=True)\n",
    "\n",
    "names_to_keep = ['Julius', 'Karen', 'Noah']\n",
    "baby_small = (baby\n",
    " .query(\"Year == 2020 and Name in @names_to_keep\")\n",
    " .reset_index(drop=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nyt_name</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Karen</td>\n",
       "      <td>boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Julius</td>\n",
       "      <td>mythology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Freya</td>\n",
       "      <td>mythology</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  nyt_name   category\n",
       "0    Karen     boomer\n",
       "1   Julius  mythology\n",
       "2    Freya  mythology"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyt_small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Noah</td>\n",
       "      <td>M</td>\n",
       "      <td>18252</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Julius</td>\n",
       "      <td>M</td>\n",
       "      <td>960</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Karen</td>\n",
       "      <td>M</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Karen</td>\n",
       "      <td>F</td>\n",
       "      <td>325</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Noah</td>\n",
       "      <td>F</td>\n",
       "      <td>305</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name Sex  Count  Year\n",
       "0    Noah   M  18252  2020\n",
       "1  Julius   M    960  2020\n",
       "2   Karen   M      6  2020\n",
       "3   Karen   F    325  2020\n",
       "4    Noah   F    305  2020"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_small"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To join tables in `pandas`, we'll use the `.merge()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Count</th>\n",
       "      <th>Year</th>\n",
       "      <th>nyt_name</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Julius</td>\n",
       "      <td>M</td>\n",
       "      <td>960</td>\n",
       "      <td>2020</td>\n",
       "      <td>Julius</td>\n",
       "      <td>mythology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Karen</td>\n",
       "      <td>M</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>Karen</td>\n",
       "      <td>boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Karen</td>\n",
       "      <td>F</td>\n",
       "      <td>325</td>\n",
       "      <td>2020</td>\n",
       "      <td>Karen</td>\n",
       "      <td>boomer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name Sex  Count  Year nyt_name   category\n",
       "0  Julius   M    960  2020   Julius  mythology\n",
       "1   Karen   M      6  2020    Karen     boomer\n",
       "2   Karen   F    325  2020    Karen     boomer"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_small.merge(nyt_small,\n",
    "                 left_on='Name',        # column in left table to match\n",
    "                 right_on='nyt_name')   # column in right table to match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the new table has the columns of both the `baby_small` and `nyt_small`\n",
    "tables. The rows with the name `Noah` are gone. And the remaining rows have their\n",
    "matching `category` from `nyt_small`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{note}\n",
    "\n",
    "Readers should also be aware that `pandas` has a `.join()` method for joining two dataframes together. However, the `.merge()` method has more flexibility for how the dataframes are joined, which is why we focus on `.merge()`. We encourage readers to consult the `pandas` documentation for the exact difference between the two.\n",
    "\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we join two tables together, we tell `pandas` the column(s) from each\n",
    "table that we want to use to make the join (the `left_on` and `right_on` arguments).\n",
    "`pandas` matches rows together when the values in the joining columns match, as\n",
    "shown in {numref}`fig:inner-join`.\n",
    "\n",
    "```{figure} figures/inner-join.svg\n",
    "---\n",
    "name: fig:inner-join\n",
    "alt: inner-join\n",
    "---\n",
    "To join, `pandas` matches rows using the values in the `Name` and `nyt_name`\n",
    "columns, dropping rows that don't have matching values\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, `pandas` does an *inner join*. If either table has rows that don't\n",
    "have matches in the other table, `pandas` drops those rows from the result. In\n",
    "this case, the `Noah` rows in `baby_small` don't have matches in\n",
    "`nyt_small`, so they are dropped. Also, the `Freya` row in `nyt_small`\n",
    "doesn't have matches in `baby_small`, so it's dropped as well. Only the\n",
    "rows with a match in both tables stay in the final result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Left, Right, and Outer Joins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We sometimes want to keep rows without a match instead of dropping them\n",
    "entirely. There are other types of joins---left, right, and outer---that keep\n",
    "rows even when they don't have a match.\n",
    "\n",
    "In a *left join*, rows in the left table without a match are kept in the final\n",
    "result, as shown in {numref}`fig:left-join`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{figure} figures/left-join.svg\n",
    "---\n",
    "name: fig:left-join\n",
    "alt: left-join\n",
    "---\n",
    "In a left join, rows in the left table that don't have matching values are kept\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do a left join in `pandas`, use `how='left'` in the call to `.merge()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Count</th>\n",
       "      <th>Year</th>\n",
       "      <th>nyt_name</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Noah</td>\n",
       "      <td>M</td>\n",
       "      <td>18252</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Julius</td>\n",
       "      <td>M</td>\n",
       "      <td>960</td>\n",
       "      <td>2020</td>\n",
       "      <td>Julius</td>\n",
       "      <td>mythology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Karen</td>\n",
       "      <td>M</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>Karen</td>\n",
       "      <td>boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Karen</td>\n",
       "      <td>F</td>\n",
       "      <td>325</td>\n",
       "      <td>2020</td>\n",
       "      <td>Karen</td>\n",
       "      <td>boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Noah</td>\n",
       "      <td>F</td>\n",
       "      <td>305</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name Sex  Count  Year nyt_name   category\n",
       "0    Noah   M  18252  2020      NaN        NaN\n",
       "1  Julius   M    960  2020   Julius  mythology\n",
       "2   Karen   M      6  2020    Karen     boomer\n",
       "3   Karen   F    325  2020    Karen     boomer\n",
       "4    Noah   F    305  2020      NaN        NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_small.merge(nyt_small,\n",
    "                 left_on='Name',        \n",
    "                 right_on='nyt_name',\n",
    "                 how='left')           # left join instead of inner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the `Noah` rows are kept in the final table. Since those rows didn't\n",
    "have a match in the `nyt_small` dataframe, the join leaves `NaN` values in the\n",
    "`nyt_name` and `category` columns. Also, notice that the `Freya` row in\n",
    "`nyt_small` is still dropped.\n",
    "\n",
    "A *right join* works similarly to the left join, except that nonmatching rows\n",
    "in the right table are kept instead of the left table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Count</th>\n",
       "      <th>Year</th>\n",
       "      <th>nyt_name</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Karen</td>\n",
       "      <td>M</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>Karen</td>\n",
       "      <td>boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Karen</td>\n",
       "      <td>F</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>Karen</td>\n",
       "      <td>boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julius</td>\n",
       "      <td>M</td>\n",
       "      <td>960.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>Julius</td>\n",
       "      <td>mythology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Freya</td>\n",
       "      <td>mythology</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Sex  Count    Year nyt_name   category\n",
       "0   Karen    M    6.0  2020.0    Karen     boomer\n",
       "1   Karen    F  325.0  2020.0    Karen     boomer\n",
       "2  Julius    M  960.0  2020.0   Julius  mythology\n",
       "3     NaN  NaN    NaN     NaN    Freya  mythology"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_small.merge(nyt_small,\n",
    "                 left_on='Name',        \n",
    "                 right_on='nyt_name',\n",
    "                 how='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, an *outer join* keeps rows from both tables even when they don't have\n",
    "a match:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Count</th>\n",
       "      <th>Year</th>\n",
       "      <th>nyt_name</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Noah</td>\n",
       "      <td>M</td>\n",
       "      <td>18252.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Noah</td>\n",
       "      <td>F</td>\n",
       "      <td>305.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Julius</td>\n",
       "      <td>M</td>\n",
       "      <td>960.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>Julius</td>\n",
       "      <td>mythology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Karen</td>\n",
       "      <td>M</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>Karen</td>\n",
       "      <td>boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Karen</td>\n",
       "      <td>F</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>Karen</td>\n",
       "      <td>boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Freya</td>\n",
       "      <td>mythology</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Sex    Count    Year nyt_name   category\n",
       "0    Noah    M  18252.0  2020.0      NaN        NaN\n",
       "1    Noah    F    305.0  2020.0      NaN        NaN\n",
       "2  Julius    M    960.0  2020.0   Julius  mythology\n",
       "3   Karen    M      6.0  2020.0    Karen     boomer\n",
       "4   Karen    F    325.0  2020.0    Karen     boomer\n",
       "5     NaN  NaN      NaN     NaN    Freya  mythology"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_small.merge(nyt_small,\n",
    "                 left_on='Name',        \n",
    "                 right_on='nyt_name',\n",
    "                 how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Popularity of NYT Name Categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's return to the full dataframes `baby` and `nyt`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Liam</td>\n",
       "      <td>M</td>\n",
       "      <td>19659</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Noah</td>\n",
       "      <td>M</td>\n",
       "      <td>18252</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name Sex  Count  Year\n",
       "0  Liam   M  19659  2020\n",
       "1  Noah   M  18252  2020"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .head() slices out the first few rows - convenient for saving space\n",
    "baby.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nyt_name</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lucifer</td>\n",
       "      <td>forbidden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lilith</td>\n",
       "      <td>forbidden</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  nyt_name   category\n",
       "0  Lucifer  forbidden\n",
       "1   Lilith  forbidden"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyt.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to know how the popularity of name categories in `nyt` has changed\n",
    "over time. To answer this question:\n",
    "\n",
    "1. Inner join `baby` with `nyt`.\n",
    "2. Group the table by `category` and `Year`.\n",
    "3. Aggregate the counts using a sum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>Year</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>boomer</td>\n",
       "      <td>1880</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>boomer</td>\n",
       "      <td>1881</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>boomer</td>\n",
       "      <td>1882</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>mythology</td>\n",
       "      <td>2018</td>\n",
       "      <td>2944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>mythology</td>\n",
       "      <td>2019</td>\n",
       "      <td>3320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>mythology</td>\n",
       "      <td>2020</td>\n",
       "      <td>3489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>650 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      category  Year  Count\n",
       "0       boomer  1880    292\n",
       "1       boomer  1881    298\n",
       "2       boomer  1882    326\n",
       "..         ...   ...    ...\n",
       "647  mythology  2018   2944\n",
       "648  mythology  2019   3320\n",
       "649  mythology  2020   3489\n",
       "\n",
       "[650 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cate_counts = (\n",
    "    baby.merge(nyt, left_on='Name', right_on='nyt_name') # [1]\n",
    "    .groupby(['category', 'Year'])                       # [2]\n",
    "    ['Count']                                            # [3]\n",
    "    .sum()                                               # [3]\n",
    "    .reset_index()\n",
    ")\n",
    "cate_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the popularity of `boomer` names and `mythology` names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
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   ],
   "source": [
    "boomers = px.line(cate_counts.query('category == \"boomer\"'),\n",
    "                  x='Year', y='Count')\n",
    "myths = px.line(cate_counts.query('category == \"mythology\"'),\n",
    "                x='Year', y='Count')\n",
    "\n",
    "fig = left_right(boomers, myths, width=500, height=200,\n",
    "                 subplot_titles=['Boomer names', 'Mythology names'])\n",
    "margin(fig, t=30)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the _NYT_ article claims, baby boomer names have become less popular since\n",
    "2000, while mythology names have become more popular.\n",
    "\n",
    "We can also plot the popularities of all the categories\n",
    "at once. Take a look at the following plots and see whether\n",
    "they support the claims made in the _New York Times_ article:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
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opacity: 1; font-weight: normal; white-space: pre;\">Year</text></g><g class=\"g-x4title\"/><g class=\"g-x5title\"/><g class=\"g-x6title\"/><g class=\"g-ytitle\" transform=\"translate(38.3125,0)\"><text class=\"ytitle\" transform=\"rotate(-90,-24.299999999999997,275.7)\" x=\"-24.299999999999997\" y=\"275.7\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Count</text></g><g class=\"g-y2title\"/><g class=\"g-y3title\"/><g class=\"g-y4title\" transform=\"translate(38.302734375,0)\"><text class=\"y4title\" transform=\"rotate(-90,-24.299999999999997,96.30000000000001)\" x=\"-24.299999999999997\" y=\"96.30000000000001\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Count</text></g><g class=\"g-y5title\"/><g class=\"g-y6title\"/><g class=\"annotation\" data-index=\"0\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,127.99999999999999,199.89999999999998)\"><g class=\"cursor-pointer\" transform=\"translate(66,190)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"122\" height=\"18\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"61.625\" y=\"14\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">category=forbidden</text></g></g></g><g class=\"annotation\" data-index=\"1\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,314.99999999999994,199.89999999999998)\"><g class=\"cursor-pointer\" transform=\"translate(259,190)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"110\" height=\"18\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: 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class=\"annotation-text-g\" transform=\"rotate(0,127.99999999999999,20.499999999999996)\"><g class=\"cursor-pointer\" transform=\"translate(72,11)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"111\" height=\"18\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"56.0625\" y=\"14\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">category=boomer</text></g></g></g><g class=\"annotation\" data-index=\"4\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,314.99999999999994,20.499999999999996)\"><g class=\"cursor-pointer\" transform=\"translate(257,11)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"114\" height=\"18\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"57.390625\" y=\"14\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">category=celestial</text></g></g></g><g class=\"annotation\" data-index=\"5\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,501.99999999999994,20.499999999999996)\"><g class=\"cursor-pointer\" transform=\"translate(435,11)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"132\" height=\"18\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"66.578125\" y=\"14\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">category=evangelical</text></g></g></g></g></svg>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.line(cate_counts, x='Year', y='Count',\n",
    "              facet_col='category', facet_col_wrap=3,\n",
    "              facet_row_spacing=0.15,\n",
    "              width=600, height=400)\n",
    "margin(fig, t=30)\n",
    "fig.update_yaxes(matches=None, showticklabels=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we introduced joins for dataframes.\n",
    "When joining dataframes together, we match rows using the `.merge()` function.\n",
    "It's important to consider the type of join (inner, left, right, or outer)\n",
    "when joining dataframes.\n",
    "In the next section, we'll explain how to transform values in a dataframe."
   ]
  }
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